PriApp-Install: Learning User Privacy Preferences on Mobile Apps’ Installation

Son, Ha Xuan; Carminati, Barbara; Ferrari, Elena

doi:10.1007/978-3-031-21280-2_17

Cited by 9 publications

(1 citation statement)

References 25 publications

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“…However, this can be cumbersome and may not be effective. On the other hand, an automated solution that can suggest security settings for a user based on his/her personality or privacy preference can bring better results [4]. In this paper, we introduce our privacy preference prediction solution.…”

Section: Introductionmentioning

confidence: 99%

IM2P-Medical: Towards Individual Management Privacy Preferences for the Medical Web Apps

Phien¹,

Phuong²,

Huynh³

et al. 2023

IJACSA

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With the advancement of technology, people are now able to monitor their health more efficiently. Mobile phones and smartwatches are equipped with sensors that can measure real-time changes in blood pressure, SPO2, and other attributes and public them to service providers via web applications (called web apps) for health improvement suggestions. Moreover, users can share the collected health data with other people, such as doctors, relatives, or friends. However, using technology in healthcare has raised the issue of privacy. Some health web apps, by default, intrusively gather and share data. Additionally, smartwatches may monitor people's health status 24/7. Therefore, users want to control how their health is processed (e.g., collected and shared). This can be cumbersome as they would have to configure each device manually. To address this problem, we have developed a privacy-preference prediction mechanism in the web apps called IM2P-Medical: towards Individual Management Privacy Preferences for the Medical web apps. To capture individual privacy preferences in the web apps, our model learns users' privacy behavior based on their responses in different medical scenarios. In practice, we exploited several machine learning algorithms: SVM, Gradient Boosting Classifier, Ada Boost Classifier, and Gradient Boosting Regressor. To prove the effectiveness of the proposed model, we set up several scenarios to measure the accuracy as well as the satisfaction level in the two participant groups (i.e., expert and normal users). One key point in this research's selection of participants is its focus on those living in developing countries, where privacy violation issues are not a common topic. The main contribution of our model is that it allows users to preserve their privacy without configuring privacy settings themselves.

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Section: Introductionmentioning

confidence: 99%